2_Classification_Project_Loan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's Begin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this project is about if the people who took the loan repayed it or not.\n",
    "#if target column = 1 (repaid)\n",
    "#if target column = 0 (not repaid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data\n",
    "\n",
    "#-----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "# train.csv\n",
    "# It contains training data for customers along with premium payment status (target)\n",
    "#-----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
    "# Variable\t--Definition\n",
    "# id\tUnique ID of the policy\n",
    "# perc_premium_paid_by_cash_credit\tPercentage of premium amount paid by cash or credit card\n",
    "# age_in_days\tAge in days of policy holder\n",
    "# Income\tMonthly Income of policy holder\n",
    "# Count_3-6_months_late\tNo of premiums late by 3 to 6 months\n",
    "# Count_6-12_months_late\tNo  of premiums late by 6 to 12 months\n",
    "# Count_more_than_12_months_late\tNo of premiums late by more than 12 months\n",
    "# application_underwriting_score\tUnderwriting Score of the applicant at the time of application (No applications under the score of 90 are insured)\n",
    "# no_of_premiums_paid\tTotal premiums paid on time till now\n",
    "# sourcing_channel\tSourcing channel for application\n",
    "# residence_area_type\tArea type of Residence (Urban/Rural)\n",
    "# target\t1 - premium paid on time, 0 - otherwise\n",
    "\n",
    "#-----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "# test.csv\n",
    "# Test file contains policies for which participants need to submit the probability of premium being paid on time\n",
    "\n",
    "#-----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "# Variable\tDefinition\n",
    "# id\tUnique ID of the policy\n",
    "# perc_premium_paid_by_cash_credit\tPercentage of premium amount paid by cash or credit card\n",
    "# age_in_days\tAge in days of policy holder\n",
    "# Income\tMonthly Income of policy holder\n",
    "# Count_3-6_months_late\tNo of premiums late by 3 to 6 months\n",
    "# Count_6-12_months_late\tNo  of premiums late by 6 to 12 months\n",
    "# Count_more_than_12_months_late\tNo of premiums late by more than 12 months\n",
    "# application_underwriting_score\tUnderwriting Score of the applicant at the time of application (No applications under the score of 90 are insured)\n",
    "# no_of_premiums_paid\tTotal premiums paid on time till now\n",
    "# sourcing_channel\tSourcing channel for application\n",
    "# residence_area_type\tArea type of Residence (Urban/Rural)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's import the train and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train_prize.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('test_prize.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110936</td>\n",
       "      <td>0.429</td>\n",
       "      <td>12058</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.02</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41492</td>\n",
       "      <td>0.010</td>\n",
       "      <td>21546</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31300</td>\n",
       "      <td>0.917</td>\n",
       "      <td>17531</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.69</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19415</td>\n",
       "      <td>0.049</td>\n",
       "      <td>15341</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.57</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99379</td>\n",
       "      <td>0.052</td>\n",
       "      <td>31400</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.87</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  perc_premium_paid_by_cash_credit  age_in_days  Income  \\\n",
       "0  110936                             0.429        12058  355060   \n",
       "1   41492                             0.010        21546  315150   \n",
       "2   31300                             0.917        17531   84140   \n",
       "3   19415                             0.049        15341  250510   \n",
       "4   99379                             0.052        31400  198680   \n",
       "\n",
       "   Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "0                    0.0                     0.0   \n",
       "1                    0.0                     0.0   \n",
       "2                    2.0                     3.0   \n",
       "3                    0.0                     0.0   \n",
       "4                    0.0                     0.0   \n",
       "\n",
       "   Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "0                             0.0                           99.02   \n",
       "1                             0.0                           99.89   \n",
       "2                             1.0                           98.69   \n",
       "3                             0.0                           99.57   \n",
       "4                             0.0                           99.87   \n",
       "\n",
       "   no_of_premiums_paid sourcing_channel residence_area_type  target  \n",
       "0                   13                C               Urban       1  \n",
       "1                   21                A               Urban       1  \n",
       "2                    7                C               Rural       0  \n",
       "3                    9                A               Urban       1  \n",
       "4                   12                B               Urban       1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's rename the columns to something simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.rename(columns={'perc_premium_paid_by_cash_credit':'perc','Count_3-6_months_late':'3_6','Count_6-12_months_late':'6_12','Count_more_than_12_months_late':'12_'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.rename(columns={'age_in_days':'days','application_underwriting_score':'app','no_of_premiums_paid':'p_p','sourcing_channel':'s_c','residence_area_type':'r_a_t'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
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       "      <th>0</th>\n",
       "      <td>110936</td>\n",
       "      <td>0.429</td>\n",
       "      <td>12058</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.02</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41492</td>\n",
       "      <td>0.010</td>\n",
       "      <td>21546</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31300</td>\n",
       "      <td>0.917</td>\n",
       "      <td>17531</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.69</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19415</td>\n",
       "      <td>0.049</td>\n",
       "      <td>15341</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.57</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99379</td>\n",
       "      <td>0.052</td>\n",
       "      <td>31400</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.87</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id   perc   days  Income  3_6  6_12  12_    app  p_p s_c  r_a_t  target\n",
       "0  110936  0.429  12058  355060  0.0   0.0  0.0  99.02   13   C  Urban       1\n",
       "1   41492  0.010  21546  315150  0.0   0.0  0.0  99.89   21   A  Urban       1\n",
       "2   31300  0.917  17531   84140  2.0   3.0  1.0  98.69    7   C  Rural       0\n",
       "3   19415  0.049  15341  250510  0.0   0.0  0.0  99.57    9   A  Urban       1\n",
       "4   99379  0.052  31400  198680  0.0   0.0  0.0  99.87   12   B  Urban       1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now it's much comfortable to read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>0.001</td>\n",
       "      <td>27384</td>\n",
       "      <td>51150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>0.124</td>\n",
       "      <td>23735</td>\n",
       "      <td>285140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.93</td>\n",
       "      <td>19</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17170</td>\n",
       "      <td>186030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.198</td>\n",
       "      <td>16068</td>\n",
       "      <td>123540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0.041</td>\n",
       "      <td>10591</td>\n",
       "      <td>200020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.17</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  perc_premium_paid_by_cash_credit  age_in_days  Income  \\\n",
       "0    649                             0.001        27384   51150   \n",
       "1  81136                             0.124        23735  285140   \n",
       "2  70762                             1.000        17170  186030   \n",
       "3  53935                             0.198        16068  123540   \n",
       "4  15476                             0.041        10591  200020   \n",
       "\n",
       "   Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "0                    0.0                     0.0   \n",
       "1                    0.0                     0.0   \n",
       "2                    0.0                     0.0   \n",
       "3                    0.0                     0.0   \n",
       "4                    1.0                     0.0   \n",
       "\n",
       "   Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "0                             0.0                           99.89   \n",
       "1                             0.0                           98.93   \n",
       "2                             0.0                             NaN   \n",
       "3                             0.0                           99.00   \n",
       "4                             0.0                           99.17   \n",
       "\n",
       "   no_of_premiums_paid sourcing_channel residence_area_type  \n",
       "0                    7                A               Rural  \n",
       "1                   19                A               Urban  \n",
       "2                    2                B               Urban  \n",
       "3                   11                B               Rural  \n",
       "4                   14                A               Rural  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.rename(columns={'perc_premium_paid_by_cash_credit':'perc','Count_3-6_months_late':'3_6','Count_6-12_months_late':'6_12','Count_more_than_12_months_late':'12_'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.rename(columns={'age_in_days':'days','application_underwriting_score':'app','no_of_premiums_paid':'p_p','sourcing_channel':'s_c','residence_area_type':'r_a_t'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
       "      <th>12_</th>\n",
       "      <th>app</th>\n",
       "      <th>p_p</th>\n",
       "      <th>s_c</th>\n",
       "      <th>r_a_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>0.001</td>\n",
       "      <td>27384</td>\n",
       "      <td>51150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>0.124</td>\n",
       "      <td>23735</td>\n",
       "      <td>285140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.93</td>\n",
       "      <td>19</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17170</td>\n",
       "      <td>186030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.198</td>\n",
       "      <td>16068</td>\n",
       "      <td>123540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0.041</td>\n",
       "      <td>10591</td>\n",
       "      <td>200020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.17</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id   perc   days  Income  3_6  6_12  12_    app  p_p s_c  r_a_t\n",
       "0    649  0.001  27384   51150  0.0   0.0  0.0  99.89    7   A  Rural\n",
       "1  81136  0.124  23735  285140  0.0   0.0  0.0  98.93   19   A  Urban\n",
       "2  70762  1.000  17170  186030  0.0   0.0  0.0    NaN    2   B  Urban\n",
       "3  53935  0.198  16068  123540  0.0   0.0  0.0  99.00   11   B  Rural\n",
       "4  15476  0.041  10591  200020  1.0   0.0  0.0  99.17   14   A  Rural"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's drop the id column from train\n",
    "train.drop('id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's normalize the columns with larger inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['days'] = (train['days']-np.mean(train['days']))/(np.std(train['days']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['days'] = (test['days']-np.mean(test['days']))/(np.std(test['days']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's check for the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "perc         0\n",
       "days         0\n",
       "Income       0\n",
       "3_6         97\n",
       "6_12        97\n",
       "12_         97\n",
       "app       2974\n",
       "p_p          0\n",
       "s_c          0\n",
       "r_a_t        0\n",
       "target       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id           0\n",
       "perc         0\n",
       "days         0\n",
       "Income       0\n",
       "3_6         31\n",
       "6_12        31\n",
       "12_         31\n",
       "app       1323\n",
       "p_p          0\n",
       "s_c          0\n",
       "r_a_t        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dealing with the null value of app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.06729119785102"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['app'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.9"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['app'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc669a6748>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['app'].plot.hist(bins=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#as we can see that most of the values are about 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['app'].fillna(value=(train['app'].mean()),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
       "      <th>12_</th>\n",
       "      <th>app</th>\n",
       "      <th>p_p</th>\n",
       "      <th>s_c</th>\n",
       "      <th>r_a_t</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429</td>\n",
       "      <td>-1.303341</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.02</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.518231</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.917</td>\n",
       "      <td>-0.252597</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.69</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.673048</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.57</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.052</td>\n",
       "      <td>2.410071</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.87</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    perc      days  Income  3_6  6_12  12_    app  p_p s_c  r_a_t  target\n",
       "0  0.429 -1.303341  355060  0.0   0.0  0.0  99.02   13   C  Urban       1\n",
       "1  0.010  0.518231  315150  0.0   0.0  0.0  99.89   21   A  Urban       1\n",
       "2  0.917 -0.252597   84140  2.0   3.0  1.0  98.69    7   C  Rural       0\n",
       "3  0.049 -0.673048  250510  0.0   0.0  0.0  99.57    9   A  Urban       1\n",
       "4  0.052  2.410071  198680  0.0   0.0  0.0  99.87   12   B  Urban       1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['app'].fillna(value=(test['app'].mean()),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "perc       0\n",
       "days       0\n",
       "Income     0\n",
       "3_6       97\n",
       "6_12      97\n",
       "12_       97\n",
       "app        0\n",
       "p_p        0\n",
       "s_c        0\n",
       "r_a_t      0\n",
       "target     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id         0\n",
       "perc       0\n",
       "days       0\n",
       "Income     0\n",
       "3_6       31\n",
       "6_12      31\n",
       "12_       31\n",
       "app        0\n",
       "p_p        0\n",
       "s_c        0\n",
       "r_a_t      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now let's check the remaining values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['3_6'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     66801\n",
       "1.0      8826\n",
       "2.0      2519\n",
       "3.0       954\n",
       "4.0       374\n",
       "5.0       168\n",
       "6.0        68\n",
       "7.0        23\n",
       "8.0        15\n",
       "9.0         4\n",
       "11.0        1\n",
       "12.0        1\n",
       "13.0        1\n",
       "10.0        1\n",
       "Name: 3_6, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['3_6'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     75831\n",
       "1.0      2680\n",
       "2.0       693\n",
       "3.0       317\n",
       "4.0       130\n",
       "5.0        46\n",
       "6.0        26\n",
       "7.0        11\n",
       "8.0         5\n",
       "10.0        4\n",
       "9.0         4\n",
       "14.0        2\n",
       "11.0        2\n",
       "13.0        2\n",
       "17.0        1\n",
       "12.0        1\n",
       "15.0        1\n",
       "Name: 6_12, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['6_12'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's replace the null values with the mod values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['3_6'].fillna(value=0.0,inplace=True)\n",
    "test['6_12'].fillna(value=0.0,inplace=True)\n",
    "test['12_'].fillna(value=0.0,inplace=True)\n",
    "train['3_6'].fillna(value=0.0,inplace=True)\n",
    "train['6_12'].fillna(value=0.0,inplace=True)\n",
    "train['12_'].fillna(value=0.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
       "      <th>12_</th>\n",
       "      <th>app</th>\n",
       "      <th>p_p</th>\n",
       "      <th>s_c</th>\n",
       "      <th>r_a_t</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429</td>\n",
       "      <td>-1.303341</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.02</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.518231</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.917</td>\n",
       "      <td>-0.252597</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.69</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.673048</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.57</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.052</td>\n",
       "      <td>2.410071</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.87</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    perc      days  Income  3_6  6_12  12_    app  p_p s_c  r_a_t  target\n",
       "0  0.429 -1.303341  355060  0.0   0.0  0.0  99.02   13   C  Urban       1\n",
       "1  0.010  0.518231  315150  0.0   0.0  0.0  99.89   21   A  Urban       1\n",
       "2  0.917 -0.252597   84140  2.0   3.0  1.0  98.69    7   C  Rural       0\n",
       "3  0.049 -0.673048  250510  0.0   0.0  0.0  99.57    9   A  Urban       1\n",
       "4  0.052  2.410071  198680  0.0   0.0  0.0  99.87   12   B  Urban       1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's normalize the app columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['app'] = (train['app']-np.mean(train['app']))/(np.std(train['app']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['app'] = (test['app']-np.mean(test['app']))/(np.std(test['app']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
       "      <th>12_</th>\n",
       "      <th>app</th>\n",
       "      <th>p_p</th>\n",
       "      <th>s_c</th>\n",
       "      <th>r_a_t</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429</td>\n",
       "      <td>-1.303341</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.065150</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.518231</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.133384</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.917</td>\n",
       "      <td>-0.252597</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.519766</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.673048</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.692544</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.052</td>\n",
       "      <td>2.410071</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.105831</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    perc      days  Income  3_6  6_12  12_       app  p_p s_c  r_a_t  target\n",
       "0  0.429 -1.303341  355060  0.0   0.0  0.0 -0.065150   13   C  Urban       1\n",
       "1  0.010  0.518231  315150  0.0   0.0  0.0  1.133384   21   A  Urban       1\n",
       "2  0.917 -0.252597   84140  2.0   3.0  1.0 -0.519766    7   C  Rural       0\n",
       "3  0.049 -0.673048  250510  0.0   0.0  0.0  0.692544    9   A  Urban       1\n",
       "4  0.052  2.410071  198680  0.0   0.0  0.0  1.105831   12   B  Urban       1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
       "      <th>12_</th>\n",
       "      <th>app</th>\n",
       "      <th>p_p</th>\n",
       "      <th>s_c</th>\n",
       "      <th>r_a_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.631539</td>\n",
       "      <td>51150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.136833e+00</td>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.936021</td>\n",
       "      <td>285140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.810720e-01</td>\n",
       "      <td>19</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.315302</td>\n",
       "      <td>186030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.252942e-12</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.198</td>\n",
       "      <td>-0.525349</td>\n",
       "      <td>123540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.497481e-02</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-1.569293</td>\n",
       "      <td>200020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.484041e-01</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id   perc      days  Income  3_6  6_12  12_           app  p_p s_c  \\\n",
       "0    649  0.001  1.631539   51150  0.0   0.0  0.0  1.136833e+00    7   A   \n",
       "1  81136  0.124  0.936021  285140  0.0   0.0  0.0 -1.810720e-01   19   A   \n",
       "2  70762  1.000 -0.315302  186030  0.0   0.0  0.0 -4.252942e-12    2   B   \n",
       "3  53935  0.198 -0.525349  123540  0.0   0.0  0.0 -8.497481e-02   11   B   \n",
       "4  15476  0.041 -1.569293  200020  1.0   0.0  0.0  1.484041e-01   14   A   \n",
       "\n",
       "   r_a_t  \n",
       "0  Rural  \n",
       "1  Urban  \n",
       "2  Urban  \n",
       "3  Rural  \n",
       "4  Rural  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DISTRIBUTIONS OF VARIOUS Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc67a13588>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9ZwKPt9NUNwHnJDmqXdg+p7VJkubJMJ86+xbgt4FvJ7mrtb0fuBy4LsklwEPAhW3bjfSmze6mN3X2YoCq2pfkI8Adrd+Hpy52S5LmxzBnQ/0Ppr/eAHD2NP0LuHSG79oGbJu76iRJB8I7uCVJnQwLSVInw0KS1MmwkCR1MiwkSZ2GOXVW0gLik5B1KAwLSQekK3QMnKXJ01CSpE6GhSSpk2EhSepkWEiSOhkWkqROzoaS9Dxf9auZOLKQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdhhYWSbYl2Zvknr62o5OMJdnVPo9q7UnyiSS7k9yd5LS+fTa2/ruSbBxWvZKkmQ1zZPEZYP1+bZuBm6tqLXBzWwc4D1jb/jYBV0EvXIAtwBnA6cCWqYCRJM2foYVFVX0T2Ldf8wZge1veDry9r/2a6rkVWJHkeOBcYKyq9lXVo8AYLw0gSdKQzfc1i+Oq6hGA9nlsaz8BeLiv30Rrm6n9JZJsSjKeZHxycnLOC5ek5WyhXODONG01S/tLG6u2VtW6qlq3cuXKOS1Okpa7+X6fxfeSHF9Vj7TTTHtb+wSwuq/fKmBPa//F/dpvmYc6JR2kQd6J8eDlF8xDJZpL8z2y2AFMzWjaCFzf1/6ONivqTODxdprqJuCcJEe1C9vntDZJ0jwa2sgiyefpjQqOSTJBb1bT5cB1SS4BHgIubN1vBM4HdgNPARcDVNW+JB8B7mj9PlxV+180lyQN2dDCoqp+a4ZNZ0/Tt4BLZ/iebcC2OSxNknSAfAf3QfA9xZKWm4UyG0qStIA5spA075wxtfg4spAkdTIsJEmdPA0laUHqOlXlaar55chCktTJsJAkdTIsJEmdDAtJUifDQpLUydlQ0/BxHpL0Yo4sJEmdDAtJUifDQpLUyWsWkhYlH0Y4vxxZSJI6GRaSpE6ehpK0ZHmqau4YFpKWNZ9uOxhPQ0mSOhkWkqROhoUkqZNhIUnqtGgucCdZD1wJHAb8cVVdPuKSJC0DzqjqWRQjiySHAX8AnAecBPxWkpNGW5UkLR+LZWRxOrC7qh4ASHItsAG4b6RVSRJz91qDhTxCWSxhcQLwcN/6BHBGf4ckm4BNbfXJJN8ZUi3HAH87pO8eFY9pcfCYFoeDPqZ8dI4rOXA/OdOGxRIWmaatXrRStRXYOvRCkvGqWjfs35lPHtPi4DEtDkvxmGCRXLOgN5JY3be+CtgzolokadlZLGFxB7A2yYlJXgFcBOwYcU2StGwsitNQVfVskn8N3ERv6uy2qrp3ROUM/VTXCHhMi4PHtDgsxWMiVdXdS5K0rC2W01CSpBEyLCRJnQyLg5DkPya5P8ndSb6cZMWoazpUSS5Mcm+SHyVZ1NP+kqxP8p0ku5NsHnU9hyrJtiR7k9wz6lrmSpLVSb6RZGf79+53R13ToUpyRJLbk/xNO6YPjbqmuWRYHJwx4M1V9bPA/wTeN+J65sI9wK8B3xx1IYdiiT4a5jPA+lEXMceeBd5TVW8CzgQuXQL/nJ4Gzqqqk4FTgPVJzhxxTXPGsDgIVfUXVfVsW72V3n0fi1pV7ayqYd31Pp+efzRMVT0DTD0aZtGqqm8C+0Zdx1yqqkeq6q/b8t8DO+k9qWHRqp4n2+rL29+SmUFkWBy6dwL/fdRF6HnTPRpmUf9HaKlLsgY4FbhttJUcuiSHJbkL2AuMVdWiP6Ypi+I+i1FI8jXg9dNs+r2qur71+T16w+nPzWdtB2uQY1oCOh8No4UjyY8DXwLeVVVPjLqeQ1VVzwGntOuYX07y5qpaEteaDIsZVNVbZ9ueZCPwy8DZtUhuVuk6piXCR8MsEkleTi8oPldVfzbqeuZSVT2W5BZ615qWRFh4GuogtBcxvRd4W1U9Nep69CI+GmYRSBLgamBnVX1s1PXMhSQrp2ZGJjkSeCtw/2irmjuGxcH5JPBqYCzJXUk+NeqCDlWSX00yAfwCcEOSm0Zd08FoEw+mHg2zE7huhI+GmRNJPg/8FfDGJBNJLhl1TXPgLcBvA2e1/w3dleT8URd1iI4HvpHkbnr/p2Wsqr4y4prmjI/7kCR1cmQhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkTv8fgHLn2IwHDAsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['days'].plot.hist(bins=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc67c32630>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['app'].plot.hist(bins=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#as we can see there are some outliers in the distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2635\n",
       "0     365\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['app']<-2]['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    72220\n",
       "0     4633\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['app']>-2]['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc68bd7438>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t/OYkaxerP0nScHYVIulbfuIivu97gb+rqt8GngbcCpwBfKGqVgFfaOsAJwKr2s864AKAJIfRe077s+g9m/2sheCRJI3HrkKkdrDcWZKDgX8DXARQVQ9U1Q+BNcDCx2YXAye35TXAJdVzDb0vPD4BOAHYWFVbq+peepcfr16MHiVJw9nVifWnJfkxvSOSR7Rl2npV1cEd3vOJwBbgb5M8DbgBeB3wuKq6m94L353ksW38Un71Mbzzrbaj+kMkWUfvKIbly5d3aFmSNMhOj0Sq6oCqOriqHlVVS9rywnqXAIFecB0NXFBVT6d3yfAZOxmfAbXaSf2hxaoLq2q2qmZnZmZ2t19J0g7szvNEFss8MF9V17b1y+mFyvfbx1S03/f0jT+ib/9lwF07qUuSxmTsIVJV3wPuTPLkVjoOuAXYACxcYbUWuKItbwBObVdpHQv8qH3sdRVwfJJD2wn141tNkjQmw37ZcLG9FvhIkgOB24BX0Au0y5KcBtwBvKyNvRJ4ETAH3NfGUlVbk7wduL6Ne1tVbR3fFCRJEwmRqvo6MDtg03EDxhZw+g5eZz2wfnG7kyQNaxLnRCRJ+whDRJLU2aTOiUjSXm3FGd5zdhgeiUiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqbGIhkuSAJF9L8r/a+sok1ybZnORj7fnrJDmorc+17Sv6XuPMVv92khMmMxNJ2n9N8kjkdcCtfevnAudV1SrgXuC0Vj8NuLeqfgs4r40jyZHAKcBRwGrgA0kOGFPvkiQmFCJJlgG/D3ywrQd4PnB5G3IxcHJbXtPWaduPa+PXAJdW1f1VdTswBxwznhlIkmByRyLvAd4I/LytPwb4YVVta+vzwNK2vBS4E6Bt/1Eb/4v6gH0kSWMw9hBJ8mLgnqq6ob88YGjtYtvO9tn+Pdcl2ZRk05YtW3arX0nSjk3iSOTZwElJvgNcSu9jrPcAhyRZ0sYsA+5qy/PAEQBt+6OBrf31Afv8iqq6sKpmq2p2ZmZmcWcjSfuxsYdIVZ1ZVcuqagW9E+NfrKo/Br4EvLQNWwtc0ZY3tHXa9i9WVbX6Ke3qrZXAKuC6MU1DkgQs2fWQsXkTcGmSdwBfAy5q9YuADyeZo3cEcgpAVd2c5DLgFmAbcHpVPTj+tiVNmxVnfGbSLewzJhoiVfX3wN+35dsYcHVVVf0MeNkO9j8bOHt0HUqSdsZvrEuSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOtubHo8rSYvCx9+Oz9iPRJIckeRLSW5NcnOS17X6YUk2Jtncfh/a6klyfpK5JN9McnTfa61t4zcnWTvuuUjS/m4SH2dtA/5zVf0OcCxwepIjgTOAL1TVKuALbR3gRGBV+1kHXAC90AHOAp5F79nsZy0EjyRpPMYeIlV1d1V9tS3/BLgVWAqsAS5uwy4GTm7La4BLquca4JAkTwBOADZW1daquhfYCKwe41Qkab830RPrSVYATweuBR5XVXdDL2iAx7ZhS4E7+3abb7Ud1SVJYzKxEEnyz4BPAK+vqh/vbOiAWu2kPui91iXZlGTTli1bdr9ZSdJAEwmRJL9GL0A+UlWfbOXvt4+paL/vafV54Ii+3ZcBd+2k/hBVdWFVzVbV7MzMzOJNRJL2c5O4OivARcCtVfWXfZs2AAtXWK0Fruirn9qu0joW+FH7uOsq4Pgkh7YT6se3miRpTCbxPZFnA/8euDHJ11vtzcA5wGVJTgPuAF7Wtl0JvAiYA+4DXgFQVVuTvB24vo17W1VtHc8UJEkwgRCpqv/N4PMZAMcNGF/A6Tt4rfXA+sXrTpK0O7ztiSSpM0NEktSZ986SNFW8L9bexSMRSVJnhogkqTNDRJLUmSEiSerMEJEkdebVWZL2Gl55NX08EpEkdWaISJI6M0QkSZ0ZIpKkzjyxLmlsPHG+7/FIRJLUmUcikhaFRxn7J49EJEmdeSQiaZc8ytCOTH2IJFkNvBc4APhgVZ0z4ZakqWNIqKupDpEkBwDvB14IzAPXJ9lQVbdMtjNp72FAaJSmOkSAY4C5qroNIMmlwBrAENFezz/u2hdMe4gsBe7sW58HnjWhXvZJ/qGTtDPTHiIZUKuHDErWAeva6j8m+faQr3848IOOvU2DfXl+zm167cvzm9jccu4ev8RvDCpOe4jMA0f0rS8D7tp+UFVdCFy4uy+eZFNVzXZvb++2L8/PuU2vfXl+++Lcpv17ItcDq5KsTHIgcAqwYcI9SdJ+Y6qPRKpqW5LXAFfRu8R3fVXdPOG2JGm/MdUhAlBVVwJXjujld/sjsCmzL8/PuU2vfXl++9zcUvWQ89CSJA1l2s+JSJImyBAZIMnLktyc5OdJZrfbdmaSuSTfTnLCpHpcLEl+N8k1Sb6eZFOSYybd02JK8tr2v9XNSd456X4WW5I3JKkkh0+6l8WS5F1JvpXkm0k+leSQSfe0p5Ksbv8/nEtyxqT7WUyGyGA3AX8IXN1fTHIkvSvAjgJWAx9ot16ZZu8E/ktV/S7wF219n5DkefTuYPDUqjoKePeEW1pUSY6gd8ufOybdyyLbCDylqp4K/ANw5oT72SN9t2c6ETgSeHn7W7JPMEQGqKpbq2rQFxLXAJdW1f1VdTswR+/WK9OsgIPb8qMZ8D2bKfZq4Jyquh+gqu6ZcD+L7TzgjQz4gu00q6rPVdW2tnoNve9/TbNf3J6pqh4AFm7PtE8wRHbPoNusLJ1QL4vl9cC7ktxJ71/qU/2vvu08CXhOkmuTfDnJMyfd0GJJchLw3ar6xqR7GbFXAp+ddBN7aF/8u/ELU3+Jb1dJPg88fsCmt1TVFTvabUBtr/9X4M7mChwH/GlVfSLJHwEXAS8YZ397YhdzWwIcChwLPBO4LMkTa0ouSdzF3N4MHD/ejhbPMP/9JXkLsA34yDh7G4Gp/LsxrP02RKqqyx/KoW6zsrfZ2VyTXAK8rq1+HPjgWJpaJLuY26uBT7bQuC7Jz+ndu2jLuPrbEzuaW5J/AawEvpEEev8//GqSY6rqe2NssbNd/feXZC3wYuC4aQn9nZjKvxvD8uOs3bMBOCXJQUlWAquA6ybc0566C/i9tvx8YPMEe1lsn6Y3J5I8CTiQfeDGflV1Y1U9tqpWVNUKen+kjp6WANmV9qC5NwEnVdV9k+5nEezTt2fab49EdibJS4D/BswAn0ny9ao6oapuTnIZveeVbANOr6oHJ9nrIngV8N4kS4Cf8cu7He8L1gPrk9wEPACs3Qf+Vbs/eB9wELCxHWldU1X/abItdbev357Jb6xLkjrz4yxJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTO/j/47VJX/jklzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['app'].plot.hist(bins=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
       "      <th>12_</th>\n",
       "      <th>app</th>\n",
       "      <th>p_p</th>\n",
       "      <th>s_c</th>\n",
       "      <th>r_a_t</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429</td>\n",
       "      <td>-1.303341</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.065150</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.518231</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.133384</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.917</td>\n",
       "      <td>-0.252597</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.519766</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.673048</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.692544</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.052</td>\n",
       "      <td>2.410071</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.105831</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    perc      days  Income  3_6  6_12  12_       app  p_p s_c  r_a_t  target\n",
       "0  0.429 -1.303341  355060  0.0   0.0  0.0 -0.065150   13   C  Urban       1\n",
       "1  0.010  0.518231  315150  0.0   0.0  0.0  1.133384   21   A  Urban       1\n",
       "2  0.917 -0.252597   84140  2.0   3.0  1.0 -0.519766    7   C  Rural       0\n",
       "3  0.049 -0.673048  250510  0.0   0.0  0.0  0.692544    9   A  Urban       1\n",
       "4  0.052  2.410071  198680  0.0   0.0  0.0  1.105831   12   B  Urban       1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's create the mapping for columns s_c and r_a_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    43134\n",
       "B    16512\n",
       "C    12039\n",
       "D     7559\n",
       "E      609\n",
       "Name: s_c, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['s_c'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['s_c'] = train['s_c'].map({'A':0,'B':1,'C':2,'D':3,'E':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
       "      <th>12_</th>\n",
       "      <th>app</th>\n",
       "      <th>p_p</th>\n",
       "      <th>s_c</th>\n",
       "      <th>r_a_t</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429</td>\n",
       "      <td>-1.303341</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.065150</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.518231</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.133384</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.917</td>\n",
       "      <td>-0.252597</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.519766</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.673048</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.692544</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.052</td>\n",
       "      <td>2.410071</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.105831</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    perc      days  Income  3_6  6_12  12_       app  p_p  s_c  r_a_t  target\n",
       "0  0.429 -1.303341  355060  0.0   0.0  0.0 -0.065150   13    2  Urban       1\n",
       "1  0.010  0.518231  315150  0.0   0.0  0.0  1.133384   21    0  Urban       1\n",
       "2  0.917 -0.252597   84140  2.0   3.0  1.0 -0.519766    7    2  Rural       0\n",
       "3  0.049 -0.673048  250510  0.0   0.0  0.0  0.692544    9    0  Urban       1\n",
       "4  0.052  2.410071  198680  0.0   0.0  0.0  1.105831   12    1  Urban       1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['s_c'] = test['s_c'].map({'A':0,'B':1,'C':2,'D':3,'E':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    18672\n",
       "1     6923\n",
       "2     5146\n",
       "3     3222\n",
       "4      261\n",
       "Name: s_c, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['s_c'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Urban    48183\n",
       "Rural    31670\n",
       "Name: r_a_t, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['r_a_t'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['r_a_t'] = train['r_a_t'].map({'Urban':0,'Rural':1})\n",
    "test['r_a_t'] = test['r_a_t'].map({'Urban':0,'Rural':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
       "      <th>12_</th>\n",
       "      <th>app</th>\n",
       "      <th>p_p</th>\n",
       "      <th>s_c</th>\n",
       "      <th>r_a_t</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429</td>\n",
       "      <td>-1.303341</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.518231</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.133384</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.917</td>\n",
       "      <td>-0.252597</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.519766</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.673048</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.692544</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.052</td>\n",
       "      <td>2.410071</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.105831</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    perc      days  Income  3_6  6_12  12_       app  p_p  s_c  r_a_t  target\n",
       "0  0.429 -1.303341  355060  0.0   0.0  0.0 -0.065150   13    2      0       1\n",
       "1  0.010  0.518231  315150  0.0   0.0  0.0  1.133384   21    0      0       1\n",
       "2  0.917 -0.252597   84140  2.0   3.0  1.0 -0.519766    7    2      1       0\n",
       "3  0.049 -0.673048  250510  0.0   0.0  0.0  0.692544    9    0      0       1\n",
       "4  0.052  2.410071  198680  0.0   0.0  0.0  1.105831   12    1      0       1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc68c890f0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['days'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc69149b00>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "208847.17117703782"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Income'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Income'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's change the income columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc695174a8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(np.log(train['Income'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Income'] = np.log(train['Income'])\n",
    "test['Income'] = np.log(test['Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('target',axis=1)\n",
    "y = train['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's divide the data in train and test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=101,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DAY17\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train,y_train)\n",
    "predict = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9335804448006412\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(predict,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.20      0.43      0.27       576\n",
      "           1       0.98      0.95      0.97     19388\n",
      "\n",
      "    accuracy                           0.93     19964\n",
      "   macro avg       0.59      0.69      0.62     19964\n",
      "weighted avg       0.96      0.93      0.95     19964\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predict,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.16147642, 0.17484288, 0.18102767, 0.04915895, 0.07318106,\n",
       "       0.04375726, 0.15198079, 0.0994902 , 0.04467296, 0.02041181])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('perc', 0.16147642446103522),\n",
       " ('days', 0.174842884649824),\n",
       " ('Income', 0.18102766743725912),\n",
       " ('3_6', 0.049158951315421834),\n",
       " ('6_12', 0.07318106426743966),\n",
       " ('12_', 0.04375725620504707),\n",
       " ('app', 0.15198078779594387),\n",
       " ('p_p', 0.09949019940345069),\n",
       " ('s_c', 0.044672957069838856),\n",
       " ('r_a_t', 0.020411807394739755)]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X.columns,rfc.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc6e3b6ac8>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(train.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=130)\n",
    "rfc.fit(X,y)\n",
    "predict = rfc.predict_proba(test.drop('id',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = predict[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1 = pred>0.94"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans = pd.DataFrame(test['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans['target'] = pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans.to_csv('ans.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     24230\n",
       "False     9994\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train['3_6']>1.5, '3_6'] = np.mean(train['3_6'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train['6_12']>5, '6_9'] = np.mean(train['6_12'])\n",
    "train.loc[train['12_']>4, '12_'] = np.mean(train['12_'])\n",
    "test.loc[test['3_6']>1.5, '3_6'] = np.mean(test['3_6'])\n",
    "test.loc[train['6_12']>5, '6_9'] = np.mean(test['6_12'])\n",
    "test.loc[train['12_']>4, '12_'] = np.mean(test['12_'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train['app']<-2, 'app'] = np.max(train['app'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.loc[test['app']<-2, 'app'] = np.max(test['app'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('target',axis=1)\n",
    "y = train['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
       "      <th>12_</th>\n",
       "      <th>app</th>\n",
       "      <th>p_p</th>\n",
       "      <th>s_c</th>\n",
       "      <th>r_a_t</th>\n",
       "      <th>target</th>\n",
       "      <th>6_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429</td>\n",
       "      <td>-1.303341</td>\n",
       "      <td>12.780042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.065150</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.518231</td>\n",
       "      <td>12.660804</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.133384</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.917</td>\n",
       "      <td>-0.252597</td>\n",
       "      <td>11.340237</td>\n",
       "      <td>0.248369</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.519766</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.673048</td>\n",
       "      <td>12.431254</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.692544</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.052</td>\n",
       "      <td>2.410071</td>\n",
       "      <td>12.199451</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.105831</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    perc      days     Income       3_6  6_12  12_       app  p_p  s_c  r_a_t  \\\n",
       "0  0.429 -1.303341  12.780042  0.000000   0.0  0.0 -0.065150   13    2      0   \n",
       "1  0.010  0.518231  12.660804  0.000000   0.0  0.0  1.133384   21    0      0   \n",
       "2  0.917 -0.252597  11.340237  0.248369   3.0  1.0 -0.519766    7    2      1   \n",
       "3  0.049 -0.673048  12.431254  0.000000   0.0  0.0  0.692544    9    0      0   \n",
       "4  0.052  2.410071  12.199451  0.000000   0.0  0.0  1.105831   12    1      0   \n",
       "\n",
       "   target  6_9  \n",
       "0       1  NaN  \n",
       "1       1  NaN  \n",
       "2       0  NaN  \n",
       "3       1  NaN  \n",
       "4       1  NaN  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('6_9',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc</th>\n",
       "      <th>days</th>\n",
       "      <th>Income</th>\n",
       "      <th>3_6</th>\n",
       "      <th>6_12</th>\n",
       "      <th>12_</th>\n",
       "      <th>app</th>\n",
       "      <th>p_p</th>\n",
       "      <th>s_c</th>\n",
       "      <th>r_a_t</th>\n",
       "      <th>6_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.631539</td>\n",
       "      <td>10.842518</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.136833e+00</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.936021</td>\n",
       "      <td>12.560736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.810720e-01</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.315302</td>\n",
       "      <td>12.133663</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.252942e-12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.198</td>\n",
       "      <td>-0.525349</td>\n",
       "      <td>11.724320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.497481e-02</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-1.569293</td>\n",
       "      <td>12.206173</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.484041e-01</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id   perc      days     Income  3_6  6_12  12_           app  p_p  s_c  \\\n",
       "0    649  0.001  1.631539  10.842518  0.0   0.0  0.0  1.136833e+00    7    0   \n",
       "1  81136  0.124  0.936021  12.560736  0.0   0.0  0.0 -1.810720e-01   19    0   \n",
       "2  70762  1.000 -0.315302  12.133663  0.0   0.0  0.0 -4.252942e-12    2    1   \n",
       "3  53935  0.198 -0.525349  11.724320  0.0   0.0  0.0 -8.497481e-02   11    1   \n",
       "4  15476  0.041 -1.569293  12.206173  1.0   0.0  0.0  1.484041e-01   14    0   \n",
       "\n",
       "   r_a_t  6_9  \n",
       "0      1  NaN  \n",
       "1      0  NaN  \n",
       "2      0  NaN  \n",
       "3      1  NaN  \n",
       "4      1  NaN  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop('6_9',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('target',axis=1)\n",
    "y = train['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=70)\n",
    "rfc.fit(X,y)\n",
    "predict = rfc.predict_proba(test.drop('id',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = predict[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1 = pred>0.93"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans = pd.DataFrame(test['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans['target'] = pred1\n",
    "ans.to_csv('ans.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     24917\n",
       "False     9307\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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